Triple
T7338678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Farey tessellation |
E169192
|
entity |
| Predicate | hasFundamentalDomain |
P77198
|
FINISHED |
| Object | ideal triangle with vertices 0,1,∞ |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: ideal triangle with vertices 0,1,∞ | Statement: [Farey tessellation, hasFundamentalDomain, ideal triangle with vertices 0,1,∞]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFundamentalDomain Context triple: [Farey tessellation, hasFundamentalDomain, ideal triangle with vertices 0,1,∞]
-
A.
isFundamental
Indicates that something is a basic, essential, or foundational element upon which other things depend or are built.
-
B.
hasBasisIn
Indicates that one entity is founded, derived, or justified on the grounds of another entity.
-
C.
hasComplexEmbeddings
Indicates that an entity is associated with or represented by complex-valued vector embeddings in some embedding space.
-
D.
hasHyperbolicArea
Indicates that one entity possesses or is associated with a specific area measured in hyperbolic geometry.
-
E.
hasCoreArea
Indicates that an entity possesses a primary or central area that is fundamental to its structure, function, or focus.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c68a57710481909f0c1f3c6ebdb6f2 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f347f25081908e6086d4073295f5 |
completed | March 27, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69c6f028fd748190b2ea5c3081958a42 |
completed | March 27, 2026, 9:01 p.m. |
| PDg | Predicate description generation | batch_69c6f3463d0481908aed9ed43a8ac6a8 |
completed | March 27, 2026, 9:14 p.m. |
Created at: March 27, 2026, 3:04 p.m.